About the Client
Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building.
About the Role
As a Member of Technical Staff, Machine Learning, you'll build core ML components and work on real production systems from day one — learning how large-scale ML behaves outside of research settings. This role is for engineers who want to develop strong systems judgment by shipping, debugging, and iterating on real-world ML.
What You'll Work On




- Build and improve ML components across data, training, evaluation, and inference
- Fine-tune and adapt models as part of larger production systems
- Implement evaluation and testing to understand model behavior
- Help build and maintain data pipelines for real-world and synthetic data
- Debug model issues, performance problems, and production incidents
- Ship improvements iteratively and learn from real user feedback
- Work closely with senior ML engineers and product teams
- Work under real production constraints: latency, cost, reliability, and safety

Requirements
- Strong foundations in machine learning and modern neural architectures
- Some hands-on experience training, fine-tuning, or deploying ML models
- Comfortable writing production-quality code and learning new tools quickly
- Curious, coachable, and eager to learn from real production systems
- Able to work through ambiguity with guidance, growing ownership over

📌 Member of Technical Staff, Machine Learning (Madrid)
🏢 DNA325
📍 Madrid